Modern manufacturing of semiconductor devices involves high volume production environments. One such environment involves populating circuit boards. Circuit boards are populated by automated machines generally referred to as “pick and place” machines. Generally, a pick and place machine acquires a semiconductor component from a tray of components, locates the component and places it on the circuit board. The function of locating the semiconductor component is carried out by a computer vision system. Typically the vision system locates a single specific semiconductor component at a time.
Prior to operation an operator often must program or train the vision system to recognize the particular semiconductor component to be located and placed on the circuit board. This involves creation of a part model. The part model represents characteristic or unique features of the semi-conductor device. For example, if the device is a leaded device, the part model may include information about the number of leads, their size, their pitch and other related features.
While many different types of vision systems are available, each involves some appreciable amount of operator input. This is because even though there may be comparably few types of semiconductor components, e.g. leaded components, ball grid arrays and odd form devices, there are hundreds, if not thousands of different sizes and configurations of components for any given component type. Thus, a user cannot just simply inform the vision system the type of component, the user must exhaustively describe the component. While many different techniques exist to assist users in making this description users nonetheless often make mistakes. A need exists to further minimize the amount of user input when training vision systems to recognize different semiconductor components.